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With the increased life expectancy of people with cystic fibrosis (CF), clinical attention has focused on prevention and treatment of non-pulmonary comorbidities. CF-related bone disease (CFBD) is a common complication and leads to increased fracture rates. Dual energy X-ray absorptiometry (DXA) is the recommended and gold standard technique to identify and monitor bone health. However, DXA has limitations because of its two-dimensional nature. Complementary tools to DXA are available, such as trabecular bone score (TBS) and vertebral fracture assessment (VFA). Quantitative computed tomography (QCT), magnetic resonance imaging (MRI) and quantitative ultrasound (QUS) may also be useful.

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BACKGROUND: Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE: We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. METHODS: A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated. RESULTS: The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. CONCLUSIONS: Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

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Among those who use of liver-enzyme inducing anticonvulsant medication for more than 2 years, 27% have a prevalent vertebral fracture on vertebral fracture assessment (VFA) lateral spine imaging. VFA imaging at the time of bone densitometry may be appropriate for older individuals who are chronic users of these medications. PURPOSE: It is unclear whether prevalent vertebral fractures are associated with use of anticonvulsant drugs, especially those that induce liver enzymes (LEI) that metabolize drugs and vitamin D. Our purpose was to estimate the prevalence of vertebral fracture on densitometric lateral spine images according to duration of prior anticonvulsant medication use. METHODS: Our study population was 11,822 individuals (mean [sd] age 76.1 [6.8] years, 94% female) who had bone densitometry with VFA between 2010 and 2018. Cumulative prior exposure to LEI anticonvulsants (carbamazepine, phenobarbital, phenytoin, valproic acid, n = 538), non-LEI anticonvulsants (clonazepam, gabapentin, levetiracetam, others, n = 2786), and other non-clonazepam benzodiazepines (n = 5082) was determined using linked pharmacy records. Prevalent vertebral fractures were identified on VFA images using the modified ABQ method. Logistic regression models were used to estimate the association of anticonvulsant drug exposure with prevalent vertebral fractures. RESULTS: Prevalence of one or more vertebral fractures was 16.1% for the entire analytic cohort, and 27.0%, 19.0%, and 18.5% for those with ≥ 2 years of prior LEI anticonvulsant use, non-LEI anticonvulsant use, and other benzodiazepine use, respectively. Adjusted for multiple covariates, use of prior LEI anticonvulsant medication for ≥ 2 years was associated with prevalent fracture on VFA (OR 1.48 [95% CI 1.04, 2.10]). CONCLUSION: LEI anticonvulsant use for ≥ 2 years is associated with higher vertebral fracture prevalence. Lateral spine VFA imaging at the time of bone densitometry may be appropriate for older individuals who have used LEI anticonvulsant medications for ≥ 2 years.

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The identification of vertebral fracture is a key point in an FLS. We have analyzed the characteristics of 570 patients according to the route of identification (referral by other doctors, emergency registry or through VFA), concluding that promoting referral by other doctors with a training campaign is effective. PURPOSE: Vertebral fractures (VF) are associated with increased risk of further VFs. Our objective was to analyze the characteristics of patients with VF seen in a Fracture Liaison Service (FLS). METHODS: An observational study was carried out on patients with VF referred to the outpatient metabolic clinic (OMC) after a training campaign, identified in the emergency registry, and captured by VF assessment with bone densitometry (DXA-VFA) in patients with non-VFs. Patients with traumatic VF or VF > 1 year, infiltrative or neoplastic disease were excluded. The number and severity of VFs (Genant) were analyzed. Treatment initiation in the first 6 months after baseline visit was reviewed. RESULTS: Overall, 570 patients were included, mean age 73. The most common route for identifying VF was through referral to OMC (303 cases), followed by the emergency registry (198) and DXA-VFA (69). Osteoporosis by DXA was found in 312 (58%) patients and 259 (45%) had ≥ 2 VFs. The rate of grade 3 VFs was highest among patients on the emergency registry. Those identified through OMC had a higher number of VFs, a higher rate of osteoporosis, more risk factors and greater treatment initiation. Patients with VFs detected by DXA-VFA were mostly women with a single VF and had a lower rate of osteoporosis by DXA. CONCLUSIONS: We present the distribution of VFs by the route of identification in an FLS. Promoting referral by other doctors with a training campaign may help in the quality improvement of the FLS-based model of care.

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In recent years, the usefulness of dual-energy X-ray absorptiometry (DXA) as a valuable complementary method of assessing the content and distribution of adipose and lean tissue as well as bone mineral density and estimating the risk of fractures has been increasingly confirmed. The diagnosis and treatment of Cushing's syndrome remain challenging, and monitoring the effects of treatment is often necessary. DXA tests offer a potential solution to many problems related to the availability of a quick, detailed, and reliable analysis of changes in the content and distribution of individual body composition components. The article discusses total body DXA scans (FMI, VAT, ALMI), lumbar spine scans (VFA, TBS), and osteoporosis scans (BMD, T-score, Z-score)-all are of potential interest in Cushing's syndrome. The article discusses the use of the most important indicators obtained from a DXA test (FMI, VAT, ALMI, BMD, T-score, Z-score, VFA, TBS) and their clinical significance in Cushing's syndrome was verified. The literature from the last decade was used for the study, available in MEDLINE, Web of Science, and ScienceDirect.

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PURPOSE OF REVIEW: Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening. RECENT FINDINGS: A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.

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Prior non-vertebral fractures, except of the ankle, are associated with increased likelihood of vertebral fracture. As knowledge of vertebral fracture presence may alter care, vertebral fracture assessment (VFA) is indicated in patients with prior fracture. INTRODUCTION: Vertebral fractures are often unappreciated. It was recently advocated that all Fracture Liaison Service (FLS) patients have densitometric VFA performed. We evaluated the likelihood of vertebral fracture identification with VFA in patients with prior fracture using the Manitoba Bone Density database. METHODS : VFA was performed in patients with T-scores below - 1.5 and age 70 + (or younger with height loss or glucocorticoid use) obtaining bone densitometry in Manitoba from 2010 to 2018. Those with prior clinical vertebral fracture, pathologic fracture, or uninterpretable VFA were excluded. Vertebral fractures were identified using the modified ABQ method. Health records were assessed for non-vertebral fracture (excluding head, neck, hand, foot) diagnosis codes unassociated with trauma prior to DXA. Multivariable odds ratios (ORs) for vertebral fracture were estimated without and with adjustment for age, sex, body mass index, ethnicity, area of residence, income level, comorbidity score, diabetes mellitus, falls in the last year, glucocorticoid use, and lowest BMD T-score. RESULTS: The study cohort consisted of 12,756 patients (94.4% women) with mean (SD) age 75.9 (6.8) years. Vertebral fractures were identified in 1925 (15.1%) overall. Vertebral fractures were significantly more likely (descending order) in those with prior pelvis, hip, humerus, other sites, and forearm, but not ankle fracture. There was modest attenuation with covariate adjustment but statistical significance was maintained. CONCLUSIONS: Prior hip, humerus, pelvis, forearm, and other fractures are associated with an increased likelihood of previously undiagnosed vertebral fracture, information useful for risk stratification and monitoring. These data support recommending VFA in FLS patients who are age 70 + with low BMD.

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Vertebral fractures (VF) are common in older men but data on VF prevalence in young men is limited. The aim of this study was to describe the prevalence of VF and non-fracture vertebral deformities (VD) in healthy young to middle-aged men, and compare the characteristics of men with normal vertebrae, VF and VD. In this cross-sectional study, vertebral fracture assessment by dual-energy X-ray absorptiometry was performed in 650 men, aged 32 to 60 years (mean 46.2), from the population-based SIBLOS-SIBEX cohort. For VF and VD assessment, both the modified algorithm-based qualitative approach (morphologic criteria) to discriminate VF from VD and the semi-quantitative (morphometric) grading system of Genant (GSQ) were used. We found 48 (0.6%) fractured vertebrae, of which 15 were classified grade 1, 29 grade 2 and 4 grade 3 VF. There were 378 (4.7%) VD, of which 296 were scored grade 1, 82 grade 2 and none grade 3 VD. Twenty-six participants (4%) had VF, 15 had one and 11 had 2 or more VF. Two hundred and twenty-eight (35.1%) men had VD. Femoral neck, total hip and lumbar spine areal bone mineral density (aBMD) were lower in men with VF than in those with normal vertebrae or VD. Men with VD, in turn, had aBMD values similar to men with normal vertebrae. Our results suggest that -even in young healthy men-using the GSQ without taking qualitative aspects into account overestimates VF prevalence, confirming the importance of morphologic criteria to correctly diagnose and distinguish VF from VD.

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In subjects older than 50 years, the presence of clinical risk factors (CRFs) for fractures or a recent fracture is the cornerstone for case finding. In patients who are clinically at high short- and long-term risk of fractures (those with a recent clinical fracture or with multiple CRFs), further assessment with bone mineral density (BMD) measurement using dual-energy absorptiometry (DXA), imaging of the spine, fall risk evaluation and laboratory examination contributes to treatment decisions according to the height and modifiability of fracture risk. Treatment is available with anti-resorptive and anabolic drugs, and from the start of treatment a lifelong strategy is needed to decide about continuous, intermittent, and sequential therapy. Implementation of guidelines requires further initiatives for improving case finding, public awareness about osteoporosis and national policies on reimbursement of assessment and therapy.

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BACKGROUND: Convolutional neural networks (CNNs) can identify vertebral compression fractures in GE vertebral fracture assessment (VFA) images with high balanced accuracy, but performance against Hologic VFAs is unknown. To obtain good classification performance, supervised machine learning requires balanced and labeled training data. Active learning is an iterative data annotation process with the ability to reduce the cost of labeling medical image data and reduce class imbalance. PURPOSE: To train CNNs to identify vertebral fractures in Hologic VFAs using an active learning approach, and evaluate the ability of CNNs to generalize to both Hologic and GE VFA images. METHODS: VFAs were obtained from the OsteoLaus Study (labeled Hologic Discovery A, n = 2726), the Manitoba Bone Mineral Density Program (labeled GE Prodigy and iDXA, n = 12,742), and the Canadian Longitudinal Study on Aging (CLSA, unlabeled Hologic Discovery A, n = 17,190). Unlabeled CLSA VFAs were split into five equal-sized partitions (n = 3438) and reviewed sequentially using active learning. Based on predicted fracture probability, 17.6% (n = 3032) of the unlabeled VFAs were selected for expert review using the modified algorithm-based qualitative (mABQ) method. CNNs were simultaneously trained on Hologic, GE dual-energy and GE single-energy VFAs. Two ensemble CNNs were constructed using the maximum and mean predicted probability from six separately trained CNNs that differed due to stochastic variation. CNNs were evaluated against the OsteoLaus validation set (n = 408) during the active learning process; ensemble performance was measured against the OsteoLaus test set (n = 819). RESULTS: The baseline CNN, prior to active learning, achieved 55.0% sensitivity, 97.9% specificity, 57.9% positive predictive value (PPV), F1-score 56.4%. Through active learning, 2942 CLSA Hologic VFAs (492 fractures) were added to the training data-increasing the proportion of Hologic VFAs with fractures from 4.2% to 12.5%. With active learning, CNN performance improved to 80.0% sensitivity, 99.7% specificity, 94.1% PPV, F1-score 86.5%. The CNN maximum ensemble achieved 91.9% sensitivity (100% for grade 3 and 95.5% for grade 2 fractures), 99.0% specificity, 81.0% PPV, F1-score 86.1%. CONCLUSION: Simultaneously training on a composite dataset consisting of both Hologic and GE VFAs allowed for the development of a single manufacturer-independent CNN that generalized to both scanner types with good classification performance. Active learning can reduce class imbalance and produce an effective medical image classifier while only labeling a subset of available unlabeled image data-thereby reducing the time and cost required to train a machine learning model.

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Vertebral fracture (VF) is a strong predictor of subsequent fracture. In this study of older women, VF, identified by dual-energy X-ray absorptiometry (DXA) vertebral fracture assessment (VFA), were associated with an increased risk of incident fractures and had a substantial impact on fracture probability, supporting the utility of VFA in clinical practice. PURPOSE: Clinical and occult VF can be identified using VFA with dual-energy X-ray absorptiometry (DXA). The aim of this study was to investigate to what extent VFA-identified VF improve fracture risk prediction, independently of bone mineral density (BMD) and clinical risk factors used in FRAX. METHODS: A total of 2852 women, 75-80 years old, from the prospective population-based study SUPERB cohort, were included in this study. At baseline, BMD was measured by DXA, VF diagnosed by VFA, and questionnaires used to collect data on risk factors for fractures. Incident fractures were captured by X-ray records or by diagnosis codes. An extension of Poisson regression was used to estimate the association between VFA-identified VF and the risk of fracture and the 5- and 10-year probability of major osteoporotic fracture (MOF) was calculated from the hazard functions for fracture and death. RESULTS: During a median follow-up of 5.15 years (IQR 4.3-5.9 years), the number of women who died or suffered a MOF, clinical VF, or hip fracture was 229, 422, 160, and 124, respectively. A VFA-identified VF was associated with an increased risk of incident MOF (hazard ratio [HR] = 1.78; 95% confidence interval [CI] 1.46-2.18), clinical VF (HR = 2.88; 95% [CI] 2.11-3.93), and hip fracture (HR = 1.67; 95% [CI] 1.15-2.42), adjusted for age, height, and weight. For women at age 75 years, a VFA-identified VF was associated with 1.2-1.4-fold greater 10-year MOF probability compared with not taking VFA into account, depending on BMD. CONCLUSION: Identifying an occult VF using VFA has a substantial impact on fracture probability, indicating that VFA is an efficient method to improve fracture prediction in older women.

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BACKGROUND: Type 2 diabetes (T2D) is frequently reported to be associated with an increased fracture risk. Epidemiological data on prevalent morphometric vertebral fractures (VFs) in T2D are sparse and even less is known in the prediabetic state. PURPOSE: To determine the association between prevalence and severity of morphometric VFs and glucose metabolism state: normal glucose metabolism (NGM), impaired glucose metabolism (prediabetes) or T2D. METHODS: This study included cross-sectional data from 3625 participants of the Maastricht Study who had a vertebral fracture assessment on lateral Dual Energy X-Ray Absorptiometry images. VFs were classified based on morphometric assessment into mild, moderate and severe VFs (respectively 20-24%, 25-39% or ≥40% reduction in expected vertebral body height). Logistic regression models were used to investigate the association between glucose metabolism status and the prevalence and severity of VFs. Analyses were adjusted for subject characteristics and life-style factors. RESULTS: T2D individuals were older (62.8 ± 7.5 years old) and less often female (30.5%) compared to the NGM group (57.7 ± 8.5 years old, and 58.8% female, respectively). At least one mild, moderate or severe prevalent VF was found in 8.6% of the men and 2.2% of the women in the T2D group, in 9.4% and 8.4% in the prediabetes group and in 9.1% and 4.8% in the NGM group, respectively. After adjustment T2D in women was associated with a lower probability of having a prevalent VF compared to NGM [adjusted OR 0.25 (95% CI 0.09-0.65)], while this was not the case for prediabetes. Furthermore, women with T2D had a significantly lower probability of a prevalent moderate or severe VF [adjusted OR 0.32 (95% CI 0.11-0.96)]. In men there was no significant association between T2D or prediabetes and prevalent VFs. CONCLUSION: Women with T2D had a lower probability of prevalent VFs compared to women with a normal glucose metabolism, while this was not the case for men with T2D and participants with prediabetes.

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Dedicated dual energy X-ray absorptiometry (DXA) bone mineral density (BMD) of the hip and spine are strongly associated with fractures, but it is not clear whether total body (TB) DXA measures correlate with dedicated DXA or relate to fractures. Using National Health and Nutrition Examination Survey (NHANES) data from years 2013-2014 and 2017-2018, we assessed Pearson correlations between dedicated and TB DXA measures. Associations with fractures were examined using self-reported prior fractures or fractures found on vertebral fracture assessment (VFA) using logistic regression models while controlling for age, gender, race/ethnicity, and body mass index. Among 1418 subjects from NHANES 2013-2014, we found signification correlations between all dedicated DXA BMD and TB DXA BMD measures. For dedicated spine BMD, the TB site with the strongest correlation was TB lumbar spine (r = 0.87, p < 0.001), while for dedicated total hip and femoral neck BMD, total body, pelvis, leg, and trunk BMD had the strongest correlations (r = 0.67-0.75, p < 0.001 for all). There were relatively few differences by sex or race/ethnicity. Findings were similar in 481 subjects from NHANES 2017-2018. In NHANES 2013-2014, there were 438 prior fractures in 370 subjects (26.3%). When controlling for age, gender, race/ethnicity, and body mass index, the adjusted odds ratio for fracture per T-score decrease of BMD were similar for TB BMD measures as for dedicated BMD measures (OR 1.10-1.28). In conclusion, total body DXA measures are correlated with hip and spine DXA and are strongly associated with prior fracture. Our results suggest that total body DXA measures are valid alternative sites to study BMD and fracture risk.

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CONTEXT: Vertebral fracture (VF) prevalence up to 24% has been reported among young people with type 1 diabetes (T1D). If this high prevalence is confirmed, individuals with T1D could benefit from preventative VF screening. OBJECTIVE: We compared the prevalence of VFs between adults with T1D and nondiabetic controls. METHODS: This cross-sectional study included 127 adults with T1D, and 65 controls with a similar age, sex, and BMI distribution, from outpatient clinics of 2 tertiary care centers. Vertebral fracture assessment (VFA) by dual-energy x-ray absorptiometry (DXA) was used for prevalent VFs. The modified algorithm-based qualitative (mABQ) method was applied. Bone mineral density (BMD) and trabecular bone score (TBS) were assessed by DXA. Serum bone turnover markers and sclerostin were measured in a subgroup of participants. RESULTS: Participants with T1D (70 women, 57 men) had a mean age of 42.8 ±â€…14.8 years, median diabetes duration of 25.8 (15.8-34.4) years, mean BMI of 26.6 ±â€…5.4 kg/m2 and mean HbA1c over the past 3 years of 7.5 ±â€…0.9%. Controls (35 women, 30 men) had mean age of 42.2 ±â€…15.9 years and mean BMI of 26.1 ±â€…5.1 kg/m2. VF prevalence was comparable between groups (2.4% vs 3.1%, P = 0.99). TBS, BMD at the total hip and femoral neck, and bone formation and resorption markers were lower while sclerostin levels were similar in participants with T1D vs controls. CONCLUSION: Our VFA results using the mABQ method do not confirm increased prevalence of VFs in men and women with relatively well-controlled T1D.

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With increasing life expectancy in people with Cystic fibrosis (CF), the focus of clinical care has shifted to management and prevention of non-pulmonary comorbidities. CF related bone disease, defined by low bone mineral density (BMD), is prevalent across all age groups and acknowledges the increased fractures rates that negatively impact lung function and quality of life. Dual energy X-ray absorptiometry (DXA) measurement of bone mineral content (BMC) and "areal" BMD (aBMD) is recommended for identifying and monitoring bone health in children and adults due to its low cost, low radiation exposure, and widespread availability. Recent studies in children and adolescents with chronic illness focus on adjustment of BMC and aBMD measurements for height due to the effects of short stature and delayed maturation on bone size. Expanded reference databases for alternate imaging sites such as the ultradistal radius and hip present opportunities for research and long-term monitoring. As the two-dimensional nature of DXA imposes limitations, we highlight other imaging modalities including peripheral quantitative computed tomography QCT (pQCT), magnetic resonance imaging, and quantitative ultrasound (QUS). These tools, while primarily used in a research setting, can impart information on true volumetric bone density and bone microarchitecture as well as contribute to fracture assessment and prediction. Due to the high morbidity and mortality associated with vertebral and hip fracture, we will present on vertebral fracture assessment (VFA) in both children and adults as well as applied analyses including hip structural analysis (HSA), trabecular bone score (TBS), and fracture risk assessment (FRAX) for high risk groups. Questions remain on the future clinical applicability and accessibility of these assessment and prediction tools, longitudinal monitoring through adolescence and adulthood, and how outcome measures may guide bone modifying therapies.

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PURPOSE: To investigate the time and effort needed to perform vertebral morphometry, as well as inter-observer agreement for identification of vertebral fractures on vertebral fracture assessment (VFA) images. METHODS: Ninety-six images were retrospectively selected, and three radiographers independently performed semi-automatic 6-point morphometry. Fractures were identified and graded using the Genant classification. Time needed to annotate each image was recorded, and reader fatigue was assessed using a modified Simulator Sickness Questionnaire (SSQ). Inter-observer agreement was assessed per-patient and per-vertebra for detecting fractures of all grades (grades 1-3) and for grade 2 and 3 fractures using the kappa statistic. Variability in measured vertebral height was evaluated using the intraclass correlation coefficient (ICC). RESULTS: Per-patient agreement was 0.59 for grades 1-3 fracture detection, and 0.65 for grades 2-3 only. Agreement for per-vertebra fracture classification was 0.92. Vertebral height measurements had an ICC of 0.96. Time needed to annotate VFA images ranged between 91 and 540 s, with a mean annotation time of 259 s. Mean SSQ scores were significantly lower at the start of a reading session (1.29; 95% CI: 0.81-1.77) compared to the end of a session (3.25; 95% CI: 2.60-3.90; p < 0.001). CONCLUSION: Agreement for detection of patients with vertebral fractures was only moderate, and vertebral morphometry requires substantial time investment. This indicates that there is a potential benefit for automating VFA, both in improving inter-observer agreement and in decreasing reading time and burden on readers.

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Data are still scarce regarding the prevalence and the associated factors of vertebral fractures (VF) in the North Africa and the Middle East region. In this study, VF were common in at risk Tunisian women with a prevalence of 26.19%. Lower total hip T-score, having severe back pain, and being physically inactive were independently associated with VF. INTRODUCTION: Vertebral fractures are related to a marked increase in morbidity and mortality and they are associated with a definite risk of subsequent fracture. Nevertheless, they remain underdiagnosed and little is known about their epidemiology in the African countries. In this first Tunisian study, we aimed to assess the prevalence and the associated factors of asymptomatic VF among at risk Tunisian post-menopausal women. METHODS: In this cross-sectional study, we included post-menopausal women without a previous diagnosis of VF and who were referred for bone mineral density (BMD) measurement. Each participant had had an extensive medical history investigation, a BMD assessment, and a vertebral fracture assessment (VFA) scan using a dual energy X-ray absorptiometry. VF were defined using Genant semi-quantitative method. RESULTS: Two hundred and ten post-menopausal women were included. The overall prevalence of VF was 26.19% and 9.52% of our participants had multiple VF. The prevalence of VF was significantly higher in older participants, those having a history of prior severe fragility fracture, or having at least one intrinsic fall. The percentage of low bone mineral density and osteoporosis were significantly higher in women with VF. After binary logistic regression analysis, severe back pain (OR = 3.016; 95% CI 1.304-6.974), regular physical activity (OR = 0.065; 95% CI 0.02-0.213), and total hip T-score (OR = 0.56; 95% CI 0.383-0.820) were independently associated with VF. CONCLUSION: VF are very prevalent among at risk Tunisian post-menopausal women and their incorporation in a clinical and densitometric tool might identify more effectively subsequent fracture.

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This study developed a prediction model to assess the need for asymptomatic osteoporotic vertebral compression fracture (OVCF) screening in women without using clinical risk factors. Our results demonstrated that the combination of age, height loss, and femoral neck T-score can predict OVCF comparable to previous models, including FRAX. PURPOSE: Osteoporotic vertebral compression fracture (OVCF) is a major fracture in osteoporosis patients. Early detection of OVCF can reduce the risk of subsequent fractures and death. Many existing diagnostic tools can screen for the risk of osteoporotic fracture but none aim to identify OVCF. The objective of this research is to study a predictive model for capturing OVCF and compare it with previous models. METHODS: A retrospective review was conducted that included women aged ≥ 50 years who underwent dual-energy X-ray absorptiometry and vertebral fracture screening between 2012 and 2019. The data included age, height, weight, history of height loss (HHL), and bone mass density (BMD). Receiver operating characteristic analysis and univariate and multivariate logistic regression were performed. The predictive OVCF model was formulated, and the result was compared to other models. RESULTS: A total of 617 women, a 179 of which had OVCFs, were eligible for analysis. Multivariate regression analysis showed age > 65, height loss > 1.5 cm, and femoral neck T-score < -1.7 as independent risk factors for OVCF. This model revealed comparable performance with FRAX. The model without BMD revealed superior performance to FRAX and other standard osteoporosis assessment models. CONCLUSIONS: BMD and vertebral fracture screening should be eligible for individual women age > 65 years with an HHL more than 1.5 cm, regardless of BMD. Vertebral fracture assessment should be additionally conducted on these women with a femoral neck T-score less than -1.7.

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BACKGROUND: Vertebral fracture assessment (VFA) images are acquired in dual-energy (DE) or single-energy (SE) scan modes. Automated identification of vertebral compression fractures, from VFA images acquired using GE Healthcare scanners in DE mode, has achieved high accuracy through the use of convolutional neural networks (CNNs). Due to differences between DE and SE images, it is uncertain whether CNNs trained on one scan mode will generalize to the other. PURPOSE: To evaluate the ability of CNNs to generalize between GE DE and GE SE VFA scan modes. METHODS: 12,742 GE VFA images from the Manitoba Bone Mineral Density Program, obtained between 2010 and 2017, were exported in both DE and SE modes. VFAs were classified by imaging specialists as fracture present or absent using the modified algorithm-based qualitative (mABQ) method. VFA scans were randomly divided into independent training (60%), validation (10%), and test (30%) sets. Three CNN models were constructed by training separately on DE only, SE only, and a composite dataset comprised of both SE and DE VFAs. All three trained CNN models were separately evaluated against both SE and DE test datasets. RESULTS: Good performance was seen for CNNs trained and evaluated on the same scan mode. DE scans used for both training and evaluation (DE/DE) achieved 87.9% sensitivity, 87.4% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.94. SE scans used for both training and evaluation (SE/SE) achieved 78.6% sensitivity, 90.6% specificity, AUC = 0.92. Conversely, CNNs performed poorly when evaluated on scan modes that differed from their training sets (AUC = 0.58). However, a composite CNN trained simultaneously on both SE and DE VFAs gave performance comparable to DE/DE (82.4% sensitivity, 94.3% specificity, AUC = 0.95); and provided improved performance over SE/SE (82.2% sensitivity, 92.3% specificity, AUC = 0.94). Positive predictive value was higher with the composite CNN compared with models trained solely on DE (74.5% vs. 58.7%) or SE VFAs (68.6% vs. 62.9%). CONCLUSION: CNNs for vertebral fracture identification are highly sensitive to scan mode. Training CNNs on a composite dataset, comprised of both GE DE and GE SE VFAs, allows CNNs to generalize to both scan modes and may facilitate the development of manufacturer-independent machine learning models for vertebral fracture detection.

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